{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-hierarchical-bayesn-inference","title":"Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift","arxiv_id":"2401.08755","date":"2024-01-16","proceeding":null,"authors":["Matthew Grayling","Stephen Thorp","Kaisey S. Mandel","Suhail Dhawan","Ana Sofia M. Uzsoy","Benjamin M. Boyd","Erin E. Hayes","Sam M. Ward"],"abstract":"We apply the hierarchical probabilistic SED model BayeSN to analyse a sample of 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law $R_V$ population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of $-0.049\\pm0.016$ mag, at a significance of 3.1$\\sigma$, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding $\\sim$2$\\sigma$ differences in magnitude and colour around peak and 4.5$\\sigma$ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean $R_V$ of $\\sim$2$\\sigma$ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the $R_V$ distribution to linearly evolve with redshift but find no evidence for any evolution - we infer the gradient of this relation $\\eta_R = -0.38\\pm0.70$. In addition, we discuss in brief a new, GPU-accelerated Python implementation of BayeSN suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn.","url_abs":"https://arxiv.org/abs/2401.08755v2","url_pdf":"https://arxiv.org/pdf/2401.08755v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"scalable-hierarchical-bayesn-inference","repo_url":"https://github.com/bayesn/bayesn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}